Machine learning

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For the journal, see Machine Learning (journal).


Machine learning is a discipline and artform in which computer programs learn from exposure to data. It uses algorithms to detect patterns, formulate predictions, learn from data, and make decisions. In the case of COVID-19, machine learning is used for the diagnosis and identification of the population that is at greater risk of contagion. It is also used for faster drug development, including the study of reuse of drugs that have been proven to treat other diseases. To do this, knowledge graphs are constructed and predictive analysis of the interaction between drug and viral proteins [1] and virus-host interactomes [2] predictive protein folding [3] understanding the molecular and cellular dynamics of the virus, predicting and spreading a disease based on patterns, and even predicting an upcoming zoonotic pandemic.

To conduct AI studies using machine learning (which includes deep learning in some cases), certain algorithms are required, such as decision trees, regression for statistical and predictive analysis, generative adversary networks, instance-based clustering, Bayesians, neural networks, etc. These algorithms use data science in which various mathematical calculations are performed, where the density of information is broad, complex and varied. For example, used to find antiviral molecules [4] that fight COVID-19 and identify millions of antibodies for the treatment of secondary infections [5]

Machine learning is defined as "a type of artificial intelligence that enable computers to independently initiate and execute learning when exposed to new data"[6][7].

Machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". At a general level, machine learning can be classified by:

  • Inductive. Inductive machine learning methods extract rules and patterns out of massive data sets.
  • Deductive.

Machine learning can also be classified by:

  • Supervised machine learning which is "used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data." (italics added)[8]
  • Unsupervised machine learning which is "used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data. (italics added)"[9]

The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods. Hence, machine learning is closely related to data mining and statistics but also theoretical computer science.

Machine learning has a wide spectrum of applications including natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.

A checklist has been developed for determining whether a clinical algorithm derived from machine learning should be considered clinically useful[10].


Machine learning opens up a myriad of research possibilities in various clinical fields. This involves everything from facial scanners to identify symptoms such as fever, wearables to measure and detect cardiac or respiratory abnormalities, to chatbots that evaluate a patient that mentions symptoms and, based on the answers given, the system informs the person if the next recommended action would be staying home, calling the doctor, or going to the hospital[11]

Risk Factors

Another type of machine learning application revolves around the prediction of infection risks, based on specific characteristics of a person, such as age, geographical location, socioeconomic level, social and hygiene habits, pre-existing conditions and human interaction, among others. With these data, a predictive model can be established on the risk that an individual or group of people can bring to contract COVID-19 and factors associated with developing complications [12] and even predict the results of a treatment. With these types of projections, you could literally predict whether a patient lives or dies.


The advantage of using machine learning over other standard techniques that take years is that the identification process can be completed in a matter of weeks, with a considerable cost reduction, coupled with a very high probability of success. For example, Smith and Smith [13] state that the future design of SARS-CoV-2 antiviral drugs is already in the hands of a European team that uses the IBM supercomputer equipped with the AI ​​SUMMIT system to be used in treatments for COVID-19.

Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine. Human intuition cannot be entirely eliminated since the designer of the system must specify how the data are to be represented and what mechanisms will be used to search for a characterization of the data. Machine learning can be viewed as an attempt to automate parts of the scientific method.

Some statistical machine learning researchers create methods within the framework of Bayesian statistics.

Algorithm types

Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm. Common algorithm types include:[14]

  • Supervised learning — in which the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate) the behavior of a function which maps a vector <math>[X_1, X_2, \ldots X_N]\,</math> into one of several classes by looking at several input-output examples of the function.
    • Support vector machine is "supervised machine learning algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples"[15].
  • Unsupervised learning — which models a set of inputs: labeled examples are not available.
  • Semi-supervised learning — which combines both labeled and unlabeled examples to generate an appropriate function or classifier.
  • Reinforcement learning — in which the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm.
  • Transduction — similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and test inputs which are available while training.
  • Learning to learn — in which the algorithm learns its own inductive bias based on previous experience.

Deep learning, a type of computer or artificial neural networks, is "supervised or unsupervised machine learning methods that use multiple layers of data representations generated by nonlinear transformations, instead of individual task-specific algorithms, to build and train neural network models"[16].

  • Convolutional neural network (CNN; ConvNet), also called shift invariant or space invariant artificial neural networks (SIANN), used for visual imagery such as retinal scans[17].

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.

Machine learning topics

This list represents the topics covered on a typical machine learning course.
Approximate inference techniques
  • Most of methods listed above either use optimization or are instances of optimization algorithms
Meta-learning (ensemble methods)
Inductive transfer and learning to learn

See also


  1. . doi:10.1186/s12864-018-4924-2. Check |doi= value (help). Missing or empty |title= (help)
  2. . doi:10.1128/mSystems.00303-18. Check |doi= value (help). Missing or empty |title= (help)
  3. . doi:10.3390/biom10020250. Check |doi= value (help). Missing or empty |title= (help)
  4. . doi:10.1016/j.imr.2020.100434. Check |doi= value (help). Missing or empty |title= (help)
  5. . doi:10.1016/j.drudis.2020.04.005. Check |doi= value (help). Missing or empty |title= (help)
  6. Anonymous (2023), Machine learning (English). Medical Subject Headings. U.S. National Library of Medicine.
  7. Liu Y, Chen PC, Krause J, Peng L (2019). "How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature". JAMA. 322 (18): 1806–1816. doi:10.1001/jama.2019.16489. PMID 31714992.
  8. Anonymous (2023), Supervised Machine Learning (English). Medical Subject Headings. U.S. National Library of Medicine.
  9. Anonymous (2023), Unsupervised Machine Learning (English). Medical Subject Headings. U.S. National Library of Medicine.
  10. Scott I, Carter S, Coiera E (2021). "Clinician checklist for assessing suitability of machine learning applications in healthcare". BMJ Health Care Inform. 28 (1). doi:10.1136/bmjhci-2020-100251. PMID 33547086 Check |pmid= value (help).
  11. . doi:10.32604/cmc.2020.010691. Check |doi= value (help). Missing or empty |title= (help)
  12. . doi:10.32604/cmc.2020.010691. Check |doi= value (help). Missing or empty |title= (help)
  13. . doi:10.26434/chemrxiv.11871402.v4. Check |doi= value (help). Missing or empty |title= (help)
  14. Sidey-Gibbons JAM, Sidey-Gibbons CJ (2019). "Machine learning in medicine: a practical introduction". BMC Med Res Methodol. 19 (1): 64. doi:10.1186/s12874-019-0681-4. PMC 6425557. PMID 30890124.
  15. Anonymous (2023), Deep learning (English). Medical Subject Headings. U.S. National Library of Medicine.
  16. Anonymous (2023), Deep learning (English). Medical Subject Headings. U.S. National Library of Medicine.
  17. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A; et al. (2016). "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs". JAMA. 316 (22): 2402–2410. doi:10.1001/jama.2016.17216. PMID 27898976.


  • Ethem Alpaydın (2004) Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press, ISBN 0262012111
  • Christopher M. Bishop (2007) Pattern Recognition and Machine Learning, Springer ISBN 0-387-31073-8.
  • Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1983), Machine Learning: An Artificial Intelligence Approach, Tioga Publishing Company, ISBN 0-935382-05-4.
  • Ryszard S. Michalski, Jaime G. Carbonell, Tom M. Mitchell (1986), Machine Learning: An Artificial Intelligence Approach, Volume II, Morgan Kaufmann, ISBN 0-934613-00-1.
  • Yves Kodratoff, Ryszard S. Michalski (1990), Machine Learning: An Artificial Intelligence Approach, Volume III, Morgan Kaufmann, ISBN 1-55860-119-8.
  • Ryszard S. Michalski, George Tecuci (1994), Machine Learning: A Multistrategy Approach, Volume IV, Morgan Kaufmann, ISBN 1-55860-251-8.
  • Bhagat, P. M. (2005). Pattern Recognition in Industry, Elsevier. ISBN 0-08-044538-1.
  • Bishop, C. M. (1995). Neural Networks for Pattern Recognition, Oxford University Press. ISBN 0-19-853864-2.
  • Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0-471-05669-3.
  • Huang T.-M., Kecman V., Kopriva I. (2006), Kernel Based Algorithms for Mining Huge Data Sets, Supervised, Semi-supervised, and Unsupervised Learning, Springer-Verlag, Berlin, Heidelberg, 260 pp. 96 illus., Hardcover, ISBN 3-540-31681-7 [1].
  • KECMAN Vojislav (2001), LEARNING AND SOFT COMPUTING, Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA, 608 pp., 268 illus., ISBN 0-262-11255-8 [2].
  • MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms, Cambridge University Press. ISBN 0-521-64298-1.
  • Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7.
  • Ian H. Witten and Eibe Frank "Data Mining: Practical machine learning tools and techniques" Morgan Kaufmann ISBN 0-12-088407-0.
  • Sholom Weiss and Casimir Kulikowski (1991). Computer Systems That Learn, Morgan Kaufmann. ISBN 1-55860-065-5.
  • Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.
  • Trevor Hastie, Robert Tibshirani and Jerome Friedman (2001). The Elements of Statistical Learning, Springer. ISBN 0387952845 (companion book site).
  • Vladimir Vapnik (1998). Statistical Learning Theory. Wiley-Interscience, ISBN 0471030031.

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